Exploiting GPU memory hierarchy for accelerating a specialized stencil computation

Summary Stencil computations are an important class of problems that can benefit from graphics processing units (GPUs). However, given the hierarchical and on‐chip blocked memory organization in GPUs, the memory performance degrades for specific data access patterns in stencils. Hence, we need appropriate data layout to effectively use the different levels of the memory to harvest the full potential of GPUs. In this context, a specialized stencil computation problem, namely, Lattice Boltzmann Method, which has a complex neighborhood relationship along with loop carried dependence, is considered as a strong case study. Four different approaches for the lattice Boltzmann method have been developed in this work by exploiting memory hierarchy with new data layouts and kernel organizations. These methods have been developed with the primary aim of increasing the compute to global memory access ratio and reducing the overall read‐write latency, even at the expense of additional computations. NVIDIA GPUs TitanX, GTX 960, GTX 740Ti, and GTX 650Ti have been used to test the proposed techniques. The compute to global memory access ratio shows an improvement of 2 to 10 times over the naive solutions in this work. The performance, in terms of time taken per iteration, is improved by up to 3.7 times. The million lattice units per second for both 2DQ9 and 3DQ19 models improve by more than 2 times.